#--------------------------------------------#
# pyclaes_ecb.py
#--------------------------------------------#

from time import time

import numpy as np
import pyopencl as cl
clmem = cl.mem_flags
clmap = cl.map_flags
clqueue = cl.command_queue_properties

# Lookup-table used for fast 32-bit AES encryption
T0 = np.array([
  0xa56363c6, 0x847c7cf8, 0x997777ee, 0x8d7b7bf6,
  0xdf2f2ff, 0xbd6b6bd6, 0xb16f6fde, 0x54c5c591,
  0x50303060, 0x3010102, 0xa96767ce, 0x7d2b2b56,
  0x19fefee7, 0x62d7d7b5, 0xe6abab4d, 0x9a7676ec,
  0x45caca8f, 0x9d82821f, 0x40c9c989, 0x877d7dfa,
  0x15fafaef, 0xeb5959b2, 0xc947478e, 0xbf0f0fb,
  0xecadad41, 0x67d4d4b3, 0xfda2a25f, 0xeaafaf45,
  0xbf9c9c23, 0xf7a4a453, 0x967272e4, 0x5bc0c09b,
  0xc2b7b775, 0x1cfdfde1, 0xae93933d, 0x6a26264c,
  0x5a36366c, 0x413f3f7e, 0x2f7f7f5, 0x4fcccc83,
  0x5c343468, 0xf4a5a551, 0x34e5e5d1, 0x8f1f1f9,
  0x937171e2, 0x73d8d8ab, 0x53313162, 0x3f15152a,
  0xc040408, 0x52c7c795, 0x65232346, 0x5ec3c39d,
  0x28181830, 0xa1969637, 0xf05050a, 0xb59a9a2f,
  0x907070e, 0x36121224, 0x9b80801b, 0x3de2e2df,
  0x26ebebcd, 0x6927274e, 0xcdb2b27f, 0x9f7575ea,
  0x1b090912, 0x9e83831d, 0x742c2c58, 0x2e1a1a34,
  0x2d1b1b36, 0xb26e6edc, 0xee5a5ab4, 0xfba0a05b,
  0xf65252a4, 0x4d3b3b76, 0x61d6d6b7, 0xceb3b37d,
  0x7b292952, 0x3ee3e3dd, 0x712f2f5e, 0x97848413,
  0xf55353a6, 0x68d1d1b9, 0x0, 0x2cededc1,
  0x60202040, 0x1ffcfce3, 0xc8b1b179, 0xed5b5bb6,
  0xbe6a6ad4, 0x46cbcb8d, 0xd9bebe67, 0x4b393972,
  0xde4a4a94, 0xd44c4c98, 0xe85858b0, 0x4acfcf85,
  0x6bd0d0bb, 0x2aefefc5, 0xe5aaaa4f, 0x16fbfbed,
  0xc5434386, 0xd74d4d9a, 0x55333366, 0x94858511,
  0xcf45458a, 0x10f9f9e9, 0x6020204, 0x817f7ffe,
  0xf05050a0, 0x443c3c78, 0xba9f9f25, 0xe3a8a84b,
  0xf35151a2, 0xfea3a35d, 0xc0404080, 0x8a8f8f05,
  0xad92923f, 0xbc9d9d21, 0x48383870, 0x4f5f5f1,
  0xdfbcbc63, 0xc1b6b677, 0x75dadaaf, 0x63212142,
  0x30101020, 0x1affffe5, 0xef3f3fd, 0x6dd2d2bf,
  0x4ccdcd81, 0x140c0c18, 0x35131326, 0x2fececc3,
  0xe15f5fbe, 0xa2979735, 0xcc444488, 0x3917172e,
  0x57c4c493, 0xf2a7a755, 0x827e7efc, 0x473d3d7a,
  0xac6464c8, 0xe75d5dba, 0x2b191932, 0x957373e6,
  0xa06060c0, 0x98818119, 0xd14f4f9e, 0x7fdcdca3,
  0x66222244, 0x7e2a2a54, 0xab90903b, 0x8388880b,
  0xca46468c, 0x29eeeec7, 0xd3b8b86b, 0x3c141428,
  0x79dedea7, 0xe25e5ebc, 0x1d0b0b16, 0x76dbdbad,
  0x3be0e0db, 0x56323264, 0x4e3a3a74, 0x1e0a0a14,
  0xdb494992, 0xa06060c, 0x6c242448, 0xe45c5cb8,
  0x5dc2c29f, 0x6ed3d3bd, 0xefacac43, 0xa66262c4,
  0xa8919139, 0xa4959531, 0x37e4e4d3, 0x8b7979f2,
  0x32e7e7d5, 0x43c8c88b, 0x5937376e, 0xb76d6dda,
  0x8c8d8d01, 0x64d5d5b1, 0xd24e4e9c, 0xe0a9a949,
  0xb46c6cd8, 0xfa5656ac, 0x7f4f4f3, 0x25eaeacf,
  0xaf6565ca, 0x8e7a7af4, 0xe9aeae47, 0x18080810,
  0xd5baba6f, 0x887878f0, 0x6f25254a, 0x722e2e5c,
  0x241c1c38, 0xf1a6a657, 0xc7b4b473, 0x51c6c697,
  0x23e8e8cb, 0x7cdddda1, 0x9c7474e8, 0x211f1f3e,
  0xdd4b4b96, 0xdcbdbd61, 0x868b8b0d, 0x858a8a0f,
  0x907070e0, 0x423e3e7c, 0xc4b5b571, 0xaa6666cc,
  0xd8484890, 0x5030306, 0x1f6f6f7, 0x120e0e1c,
  0xa36161c2, 0x5f35356a, 0xf95757ae, 0xd0b9b969,
  0x91868617, 0x58c1c199, 0x271d1d3a, 0xb99e9e27,
  0x38e1e1d9, 0x13f8f8eb, 0xb398982b, 0x33111122,
  0xbb6969d2, 0x70d9d9a9, 0x898e8e07, 0xa7949433,
  0xb69b9b2d, 0x221e1e3c, 0x92878715, 0x20e9e9c9,
  0x49cece87, 0xff5555aa, 0x78282850, 0x7adfdfa5,
  0x8f8c8c03, 0xf8a1a159, 0x80898909, 0x170d0d1a,
  0xdabfbf65, 0x31e6e6d7, 0xc6424284, 0xb86868d0,
  0xc3414182, 0xb0999929, 0x772d2d5a, 0x110f0f1e,
  0xcbb0b07b, 0xfc5454a8, 0xd6bbbb6d, 0x3a16162c], dtype=np.uint32)

T1 = np.array([
  0x6363c6a5, 0x7c7cf884, 0x7777ee99, 0x7b7bf68d,
  0xf2f2ff0d, 0x6b6bd6bd, 0x6f6fdeb1, 0xc5c59154,
  0x30306050, 0x1010203, 0x6767cea9, 0x2b2b567d,
  0xfefee719, 0xd7d7b562, 0xabab4de6, 0x7676ec9a,
  0xcaca8f45, 0x82821f9d, 0xc9c98940, 0x7d7dfa87,
  0xfafaef15, 0x5959b2eb, 0x47478ec9, 0xf0f0fb0b,
  0xadad41ec, 0xd4d4b367, 0xa2a25ffd, 0xafaf45ea,
  0x9c9c23bf, 0xa4a453f7, 0x7272e496, 0xc0c09b5b,
  0xb7b775c2, 0xfdfde11c, 0x93933dae, 0x26264c6a,
  0x36366c5a, 0x3f3f7e41, 0xf7f7f502, 0xcccc834f,
  0x3434685c, 0xa5a551f4, 0xe5e5d134, 0xf1f1f908,
  0x7171e293, 0xd8d8ab73, 0x31316253, 0x15152a3f,
  0x404080c, 0xc7c79552, 0x23234665, 0xc3c39d5e,
  0x18183028, 0x969637a1, 0x5050a0f, 0x9a9a2fb5,
  0x7070e09, 0x12122436, 0x80801b9b, 0xe2e2df3d,
  0xebebcd26, 0x27274e69, 0xb2b27fcd, 0x7575ea9f,
  0x909121b, 0x83831d9e, 0x2c2c5874, 0x1a1a342e,
  0x1b1b362d, 0x6e6edcb2, 0x5a5ab4ee, 0xa0a05bfb,
  0x5252a4f6, 0x3b3b764d, 0xd6d6b761, 0xb3b37dce,
  0x2929527b, 0xe3e3dd3e, 0x2f2f5e71, 0x84841397,
  0x5353a6f5, 0xd1d1b968, 0x0, 0xededc12c,
  0x20204060, 0xfcfce31f, 0xb1b179c8, 0x5b5bb6ed,
  0x6a6ad4be, 0xcbcb8d46, 0xbebe67d9, 0x3939724b,
  0x4a4a94de, 0x4c4c98d4, 0x5858b0e8, 0xcfcf854a,
  0xd0d0bb6b, 0xefefc52a, 0xaaaa4fe5, 0xfbfbed16,
  0x434386c5, 0x4d4d9ad7, 0x33336655, 0x85851194,
  0x45458acf, 0xf9f9e910, 0x2020406, 0x7f7ffe81,
  0x5050a0f0, 0x3c3c7844, 0x9f9f25ba, 0xa8a84be3,
  0x5151a2f3, 0xa3a35dfe, 0x404080c0, 0x8f8f058a,
  0x92923fad, 0x9d9d21bc, 0x38387048, 0xf5f5f104,
  0xbcbc63df, 0xb6b677c1, 0xdadaaf75, 0x21214263,
  0x10102030, 0xffffe51a, 0xf3f3fd0e, 0xd2d2bf6d,
  0xcdcd814c, 0xc0c1814, 0x13132635, 0xececc32f,
  0x5f5fbee1, 0x979735a2, 0x444488cc, 0x17172e39,
  0xc4c49357, 0xa7a755f2, 0x7e7efc82, 0x3d3d7a47,
  0x6464c8ac, 0x5d5dbae7, 0x1919322b, 0x7373e695,
  0x6060c0a0, 0x81811998, 0x4f4f9ed1, 0xdcdca37f,
  0x22224466, 0x2a2a547e, 0x90903bab, 0x88880b83,
  0x46468cca, 0xeeeec729, 0xb8b86bd3, 0x1414283c,
  0xdedea779, 0x5e5ebce2, 0xb0b161d, 0xdbdbad76,
  0xe0e0db3b, 0x32326456, 0x3a3a744e, 0xa0a141e,
  0x494992db, 0x6060c0a, 0x2424486c, 0x5c5cb8e4,
  0xc2c29f5d, 0xd3d3bd6e, 0xacac43ef, 0x6262c4a6,
  0x919139a8, 0x959531a4, 0xe4e4d337, 0x7979f28b,
  0xe7e7d532, 0xc8c88b43, 0x37376e59, 0x6d6ddab7,
  0x8d8d018c, 0xd5d5b164, 0x4e4e9cd2, 0xa9a949e0,
  0x6c6cd8b4, 0x5656acfa, 0xf4f4f307, 0xeaeacf25,
  0x6565caaf, 0x7a7af48e, 0xaeae47e9, 0x8081018,
  0xbaba6fd5, 0x7878f088, 0x25254a6f, 0x2e2e5c72,
  0x1c1c3824, 0xa6a657f1, 0xb4b473c7, 0xc6c69751,
  0xe8e8cb23, 0xdddda17c, 0x7474e89c, 0x1f1f3e21,
  0x4b4b96dd, 0xbdbd61dc, 0x8b8b0d86, 0x8a8a0f85,
  0x7070e090, 0x3e3e7c42, 0xb5b571c4, 0x6666ccaa,
  0x484890d8, 0x3030605, 0xf6f6f701, 0xe0e1c12,
  0x6161c2a3, 0x35356a5f, 0x5757aef9, 0xb9b969d0,
  0x86861791, 0xc1c19958, 0x1d1d3a27, 0x9e9e27b9,
  0xe1e1d938, 0xf8f8eb13, 0x98982bb3, 0x11112233,
  0x6969d2bb, 0xd9d9a970, 0x8e8e0789, 0x949433a7,
  0x9b9b2db6, 0x1e1e3c22, 0x87871592, 0xe9e9c920,
  0xcece8749, 0x5555aaff, 0x28285078, 0xdfdfa57a,
  0x8c8c038f, 0xa1a159f8, 0x89890980, 0xd0d1a17,
  0xbfbf65da, 0xe6e6d731, 0x424284c6, 0x6868d0b8,
  0x414182c3, 0x999929b0, 0x2d2d5a77, 0xf0f1e11,
  0xb0b07bcb, 0x5454a8fc, 0xbbbb6dd6, 0x16162c3a], dtype=np.uint32)

T2 = np.array([
  0x63c6a563, 0x7cf8847c, 0x77ee9977, 0x7bf68d7b,
  0xf2ff0df2, 0x6bd6bd6b, 0x6fdeb16f, 0xc59154c5,
  0x30605030, 0x1020301, 0x67cea967, 0x2b567d2b,
  0xfee719fe, 0xd7b562d7, 0xab4de6ab, 0x76ec9a76,
  0xca8f45ca, 0x821f9d82, 0xc98940c9, 0x7dfa877d,
  0xfaef15fa, 0x59b2eb59, 0x478ec947, 0xf0fb0bf0,
  0xad41ecad, 0xd4b367d4, 0xa25ffda2, 0xaf45eaaf,
  0x9c23bf9c, 0xa453f7a4, 0x72e49672, 0xc09b5bc0,
  0xb775c2b7, 0xfde11cfd, 0x933dae93, 0x264c6a26,
  0x366c5a36, 0x3f7e413f, 0xf7f502f7, 0xcc834fcc,
  0x34685c34, 0xa551f4a5, 0xe5d134e5, 0xf1f908f1,
  0x71e29371, 0xd8ab73d8, 0x31625331, 0x152a3f15,
  0x4080c04, 0xc79552c7, 0x23466523, 0xc39d5ec3,
  0x18302818, 0x9637a196, 0x50a0f05, 0x9a2fb59a,
  0x70e0907, 0x12243612, 0x801b9b80, 0xe2df3de2,
  0xebcd26eb, 0x274e6927, 0xb27fcdb2, 0x75ea9f75,
  0x9121b09, 0x831d9e83, 0x2c58742c, 0x1a342e1a,
  0x1b362d1b, 0x6edcb26e, 0x5ab4ee5a, 0xa05bfba0,
  0x52a4f652, 0x3b764d3b, 0xd6b761d6, 0xb37dceb3,
  0x29527b29, 0xe3dd3ee3, 0x2f5e712f, 0x84139784,
  0x53a6f553, 0xd1b968d1, 0x0, 0xedc12ced,
  0x20406020, 0xfce31ffc, 0xb179c8b1, 0x5bb6ed5b,
  0x6ad4be6a, 0xcb8d46cb, 0xbe67d9be, 0x39724b39,
  0x4a94de4a, 0x4c98d44c, 0x58b0e858, 0xcf854acf,
  0xd0bb6bd0, 0xefc52aef, 0xaa4fe5aa, 0xfbed16fb,
  0x4386c543, 0x4d9ad74d, 0x33665533, 0x85119485,
  0x458acf45, 0xf9e910f9, 0x2040602, 0x7ffe817f,
  0x50a0f050, 0x3c78443c, 0x9f25ba9f, 0xa84be3a8,
  0x51a2f351, 0xa35dfea3, 0x4080c040, 0x8f058a8f,
  0x923fad92, 0x9d21bc9d, 0x38704838, 0xf5f104f5,
  0xbc63dfbc, 0xb677c1b6, 0xdaaf75da, 0x21426321,
  0x10203010, 0xffe51aff, 0xf3fd0ef3, 0xd2bf6dd2,
  0xcd814ccd, 0xc18140c, 0x13263513, 0xecc32fec,
  0x5fbee15f, 0x9735a297, 0x4488cc44, 0x172e3917,
  0xc49357c4, 0xa755f2a7, 0x7efc827e, 0x3d7a473d,
  0x64c8ac64, 0x5dbae75d, 0x19322b19, 0x73e69573,
  0x60c0a060, 0x81199881, 0x4f9ed14f, 0xdca37fdc,
  0x22446622, 0x2a547e2a, 0x903bab90, 0x880b8388,
  0x468cca46, 0xeec729ee, 0xb86bd3b8, 0x14283c14,
  0xdea779de, 0x5ebce25e, 0xb161d0b, 0xdbad76db,
  0xe0db3be0, 0x32645632, 0x3a744e3a, 0xa141e0a,
  0x4992db49, 0x60c0a06, 0x24486c24, 0x5cb8e45c,
  0xc29f5dc2, 0xd3bd6ed3, 0xac43efac, 0x62c4a662,
  0x9139a891, 0x9531a495, 0xe4d337e4, 0x79f28b79,
  0xe7d532e7, 0xc88b43c8, 0x376e5937, 0x6ddab76d,
  0x8d018c8d, 0xd5b164d5, 0x4e9cd24e, 0xa949e0a9,
  0x6cd8b46c, 0x56acfa56, 0xf4f307f4, 0xeacf25ea,
  0x65caaf65, 0x7af48e7a, 0xae47e9ae, 0x8101808,
  0xba6fd5ba, 0x78f08878, 0x254a6f25, 0x2e5c722e,
  0x1c38241c, 0xa657f1a6, 0xb473c7b4, 0xc69751c6,
  0xe8cb23e8, 0xdda17cdd, 0x74e89c74, 0x1f3e211f,
  0x4b96dd4b, 0xbd61dcbd, 0x8b0d868b, 0x8a0f858a,
  0x70e09070, 0x3e7c423e, 0xb571c4b5, 0x66ccaa66,
  0x4890d848, 0x3060503, 0xf6f701f6, 0xe1c120e,
  0x61c2a361, 0x356a5f35, 0x57aef957, 0xb969d0b9,
  0x86179186, 0xc19958c1, 0x1d3a271d, 0x9e27b99e,
  0xe1d938e1, 0xf8eb13f8, 0x982bb398, 0x11223311,
  0x69d2bb69, 0xd9a970d9, 0x8e07898e, 0x9433a794,
  0x9b2db69b, 0x1e3c221e, 0x87159287, 0xe9c920e9,
  0xce8749ce, 0x55aaff55, 0x28507828, 0xdfa57adf,
  0x8c038f8c, 0xa159f8a1, 0x89098089, 0xd1a170d,
  0xbf65dabf, 0xe6d731e6, 0x4284c642, 0x68d0b868,
  0x4182c341, 0x9929b099, 0x2d5a772d, 0xf1e110f,
  0xb07bcbb0, 0x54a8fc54, 0xbb6dd6bb, 0x162c3a16], dtype=np.uint32)

T3 = np.array([
  0xc6a56363, 0xf8847c7c, 0xee997777, 0xf68d7b7b,
  0xff0df2f2, 0xd6bd6b6b, 0xdeb16f6f, 0x9154c5c5,
  0x60503030, 0x2030101, 0xcea96767, 0x567d2b2b,
  0xe719fefe, 0xb562d7d7, 0x4de6abab, 0xec9a7676,
  0x8f45caca, 0x1f9d8282, 0x8940c9c9, 0xfa877d7d,
  0xef15fafa, 0xb2eb5959, 0x8ec94747, 0xfb0bf0f0,
  0x41ecadad, 0xb367d4d4, 0x5ffda2a2, 0x45eaafaf,
  0x23bf9c9c, 0x53f7a4a4, 0xe4967272, 0x9b5bc0c0,
  0x75c2b7b7, 0xe11cfdfd, 0x3dae9393, 0x4c6a2626,
  0x6c5a3636, 0x7e413f3f, 0xf502f7f7, 0x834fcccc,
  0x685c3434, 0x51f4a5a5, 0xd134e5e5, 0xf908f1f1,
  0xe2937171, 0xab73d8d8, 0x62533131, 0x2a3f1515,
  0x80c0404, 0x9552c7c7, 0x46652323, 0x9d5ec3c3,
  0x30281818, 0x37a19696, 0xa0f0505, 0x2fb59a9a,
  0xe090707, 0x24361212, 0x1b9b8080, 0xdf3de2e2,
  0xcd26ebeb, 0x4e692727, 0x7fcdb2b2, 0xea9f7575,
  0x121b0909, 0x1d9e8383, 0x58742c2c, 0x342e1a1a,
  0x362d1b1b, 0xdcb26e6e, 0xb4ee5a5a, 0x5bfba0a0,
  0xa4f65252, 0x764d3b3b, 0xb761d6d6, 0x7dceb3b3,
  0x527b2929, 0xdd3ee3e3, 0x5e712f2f, 0x13978484,
  0xa6f55353, 0xb968d1d1, 0x0, 0xc12ceded,
  0x40602020, 0xe31ffcfc, 0x79c8b1b1, 0xb6ed5b5b,
  0xd4be6a6a, 0x8d46cbcb, 0x67d9bebe, 0x724b3939,
  0x94de4a4a, 0x98d44c4c, 0xb0e85858, 0x854acfcf,
  0xbb6bd0d0, 0xc52aefef, 0x4fe5aaaa, 0xed16fbfb,
  0x86c54343, 0x9ad74d4d, 0x66553333, 0x11948585,
  0x8acf4545, 0xe910f9f9, 0x4060202, 0xfe817f7f,
  0xa0f05050, 0x78443c3c, 0x25ba9f9f, 0x4be3a8a8,
  0xa2f35151, 0x5dfea3a3, 0x80c04040, 0x58a8f8f,
  0x3fad9292, 0x21bc9d9d, 0x70483838, 0xf104f5f5,
  0x63dfbcbc, 0x77c1b6b6, 0xaf75dada, 0x42632121,
  0x20301010, 0xe51affff, 0xfd0ef3f3, 0xbf6dd2d2,
  0x814ccdcd, 0x18140c0c, 0x26351313, 0xc32fecec,
  0xbee15f5f, 0x35a29797, 0x88cc4444, 0x2e391717,
  0x9357c4c4, 0x55f2a7a7, 0xfc827e7e, 0x7a473d3d,
  0xc8ac6464, 0xbae75d5d, 0x322b1919, 0xe6957373,
  0xc0a06060, 0x19988181, 0x9ed14f4f, 0xa37fdcdc,
  0x44662222, 0x547e2a2a, 0x3bab9090, 0xb838888,
  0x8cca4646, 0xc729eeee, 0x6bd3b8b8, 0x283c1414,
  0xa779dede, 0xbce25e5e, 0x161d0b0b, 0xad76dbdb,
  0xdb3be0e0, 0x64563232, 0x744e3a3a, 0x141e0a0a,
  0x92db4949, 0xc0a0606, 0x486c2424, 0xb8e45c5c,
  0x9f5dc2c2, 0xbd6ed3d3, 0x43efacac, 0xc4a66262,
  0x39a89191, 0x31a49595, 0xd337e4e4, 0xf28b7979,
  0xd532e7e7, 0x8b43c8c8, 0x6e593737, 0xdab76d6d,
  0x18c8d8d, 0xb164d5d5, 0x9cd24e4e, 0x49e0a9a9,
  0xd8b46c6c, 0xacfa5656, 0xf307f4f4, 0xcf25eaea,
  0xcaaf6565, 0xf48e7a7a, 0x47e9aeae, 0x10180808,
  0x6fd5baba, 0xf0887878, 0x4a6f2525, 0x5c722e2e,
  0x38241c1c, 0x57f1a6a6, 0x73c7b4b4, 0x9751c6c6,
  0xcb23e8e8, 0xa17cdddd, 0xe89c7474, 0x3e211f1f,
  0x96dd4b4b, 0x61dcbdbd, 0xd868b8b, 0xf858a8a,
  0xe0907070, 0x7c423e3e, 0x71c4b5b5, 0xccaa6666,
  0x90d84848, 0x6050303, 0xf701f6f6, 0x1c120e0e,
  0xc2a36161, 0x6a5f3535, 0xaef95757, 0x69d0b9b9,
  0x17918686, 0x9958c1c1, 0x3a271d1d, 0x27b99e9e,
  0xd938e1e1, 0xeb13f8f8, 0x2bb39898, 0x22331111,
  0xd2bb6969, 0xa970d9d9, 0x7898e8e, 0x33a79494,
  0x2db69b9b, 0x3c221e1e, 0x15928787, 0xc920e9e9,
  0x8749cece, 0xaaff5555, 0x50782828, 0xa57adfdf,
  0x38f8c8c, 0x59f8a1a1, 0x9808989, 0x1a170d0d,
  0x65dabfbf, 0xd731e6e6, 0x84c64242, 0xd0b86868,
  0x82c34141, 0x29b09999, 0x5a772d2d, 0x1e110f0f,
  0x7bcbb0b0, 0xa8fc5454, 0x6dd6bbbb, 0x2c3a1616], dtype=np.uint32)


WG_SIZE = 320

class claes_ecb(object):

    def __init__(self, profiling=False):
        ''
        self.profiling = profiling
        self._initOpenCL()


    # TODO: raise proper exceptions
    def _initOpenCL(self):
        ''
        # Get available platforms and choose the first one
        # TODO: select among available platforms?
        platforms = cl.get_platforms();
        if len(platforms) == 0:
            print("Failed to find any OpenCL platform")
            return None
        
        # Get avaible GPU devices and choose the first one
        # TODO: select among available gpus?
        devices = platforms[0].get_devices(cl.device_type.GPU)
        #devices = platforms[0].get_devices(cl.device_type.CPU)
        if len(devices) == 0:
            print("Could not find GPU devices")
            return None
        self.gpuDevice = devices[0]

        # Retrieve useful device informations
        self.wgSize = self.gpuDevice.get_info(cl.device_info.MAX_WORK_GROUP_SIZE)
        self.compUnits = self.gpuDevice.get_info(cl.device_info.MAX_COMPUTE_UNITS)
        self.maxWiSizes = self.gpuDevice.get_info(cl.device_info.MAX_WORK_ITEM_SIZES)
        # TODO: identify Nvidia platform for getting warp size
        #self.warpSize = self.gpuDevice.get_info(cl.device_info.WARP_SIZE_NV)
        self.globMemSize = self.gpuDevice.get_info(cl.device_info.GLOBAL_MEM_SIZE)
        self.locMemSize = self.gpuDevice.get_info(cl.device_info.LOCAL_MEM_SIZE)
        self.constBuffSize = self.gpuDevice.get_info(cl.device_info.MAX_CONSTANT_BUFFER_SIZE)
        self.addrBits = self.gpuDevice.get_info(cl.device_info.ADDRESS_BITS)

        # Create a context using the first GPU device found
        self.context = cl.Context([self.gpuDevice])

        # Finally create the command queue
        if self.profiling:
            self.cmdQueues = [cl.CommandQueue(self.context, self.gpuDevice, 
                                              properties=clqueue.PROFILING_ENABLE),
                              cl.CommandQueue(self.context, self.gpuDevice,
                                              properties=clqueue.PROFILING_ENABLE)]
        else:
            self.cmdQueues = [cl.CommandQueue(self.context, self.gpuDevice),
                              cl.CommandQueue(self.context, self.gpuDevice)]

        
        
    def encrypt(self, key, data, overlap=False):
        ''
        # Choose optimal parameters
        # TODO: automate choice of:
        # - bulk size;
        # - local work items size;
        # - global work items size

        # Build the kernel
        # TODO: perform only once
        kernelFile = open('aes_ecb.cl', 'r')
        kernelSrc = kernelFile.read()
        
        program = cl.Program(self.context, kernelSrc)
        program.build(devices=[self.gpuDevice])


        # Create buffers for key and T tables
        keyBuffer = cl.Buffer(self.context, clmem.READ_ONLY, size=16)

        T0buff = cl.Buffer(self.context, clmem.READ_ONLY, size=256<<2)
        T1buff = cl.Buffer(self.context, clmem.READ_ONLY, size=256<<2)
        T2buff = cl.Buffer(self.context, clmem.READ_ONLY, size=256<<2)
        T3buff = cl.Buffer(self.context, clmem.READ_ONLY, size=256<<2)


        # Split plaintext in two halves of halveSize size and overlap plaintext load into memory and
        # kernel execution
        if len(data)>16 and overlap:
    
            # Approximate the data size to the closest multiple of 256. Since each workgroup is
            # 256 items wide, if we don't have enough data for the last workgroup we fill the
            # input buffer with arbitrary data, which will be disregarded when copying back the
            # ciphertext to the result buffer
            dataSize = len(data)
            #if dataSize&0xff:
            #    roundoffSize = 256 - (dataSize&0xff)
            #    halfSize=(dataSize+roundoffSize)>>1
            if (dataSize>>5)%WG_SIZE:
                roundoffSize = WG_SIZE - ((dataSize>>5)%WG_SIZE)
                dataSize += roundoffSize<<5
                halfSize=dataSize>>1
            else:
                roundoffSize = 0
                halfSize=dataSize>>1
    
            # Init host memory buffer and device memory buffer used for enable pinned-memory
            pinInBuffer = cl.Buffer(self.context, clmem.READ_ONLY|clmem.ALLOC_HOST_PTR, dataSize)
            pinOutBuffer = cl.Buffer(self.context, clmem.WRITE_ONLY|clmem.ALLOC_HOST_PTR, dataSize)
            devInBuffer = cl.Buffer(self.context, clmem.READ_ONLY, dataSize)
            devOutBuffer = cl.Buffer(self.context, clmem.WRITE_ONLY, dataSize)

            # Get numpy arrays used for filling and retrieving data from pinned-memory
            (dataIn,ev) = cl.enqueue_map_buffer(self.cmdQueues[0], pinInBuffer, clmap.WRITE,
                                                0, (dataSize,), np.uint8, 'C')
            (dataOut,ev) = cl.enqueue_map_buffer(self.cmdQueues[0], pinOutBuffer, clmap.READ,
                                                 0, (dataSize,), np.uint8, 'C')
            
            # Fill the array obtained from memory maps
            dataIn[:len(data)] = np.frombuffer(data, dtype=np.uint8)

            
            encStartTime = time()
            
            # Copy T-tables and key into global memory
            # TODO: join in one single transfer
            evKey = cl.enqueue_copy(self.cmdQueues[0], keyBuffer, key)
            evT0 = cl.enqueue_copy(self.cmdQueues[0], T0buff, T0)
            evT1 = cl.enqueue_copy(self.cmdQueues[0], T1buff, T1)
            evT2 = cl.enqueue_copy(self.cmdQueues[0], T2buff, T2)
            evT3 = cl.enqueue_copy(self.cmdQueues[0], T3buff, T3)


            # Non-blocking copy of the first half
            # TODO: could it be blocking? actually we can't start before the first chunck is copied
            evHalf1W = cl.enqueue_copy(self.cmdQueues[0], devInBuffer, dataIn[:halfSize],
                                       is_blocking=False)
            self.cmdQueues[0].flush()


            # Launch kernel on the first half
            
            #TODO: delete
            #expkeyBuff = cl.Buffer(self.context, clmem.WRITE_ONLY, 176)
            
            evK1 = program.aes_ecb(self.cmdQueues[0], (halfSize>>4,), (WG_SIZE,), keyBuffer,
                                   devInBuffer, devOutBuffer,
                                   T0buff, T1buff, T2buff, T3buff,
                                   #T0buff, T1buff, T2buff, T3buff, expkeyBuff,
                                   np.uint32(0))
            
            
            #TODO: delete
            #expKey = np.empty(176, dtype=np.uint8)
            #cl.enqueue_copy(self.cmdQueues[0], expKey, expkeyBuff)
            #print [hex(x) for x in expKey]
            
            # Start copying the second half
            evHalf2W = cl.enqueue_copy(self.cmdQueues[1], devInBuffer, dataIn[halfSize:],
                                       device_offset=halfSize, is_blocking=False)
            
            self.cmdQueues[0].flush()
            self.cmdQueues[1].flush()
       

            # Launch kernel on the second half
            evK2 = program.aes_ecb(self.cmdQueues[1], (halfSize>>4,), (WG_SIZE,), keyBuffer,
                                   devInBuffer, devOutBuffer,
                                   T0buff, T1buff, T2buff, T3buff,
                                   np.uint32(halfSize>>4))

            # Non-blocking read of the first half
            evHalf1R = cl.enqueue_copy(self.cmdQueues[0], dataOut[:halfSize], devOutBuffer,
                                       is_blocking=False)
            
            self.cmdQueues[0].flush()
            self.cmdQueues[1].flush()

            # Finally, read the second half
            evHalf2R = cl.enqueue_copy(self.cmdQueues[1], dataOut[halfSize:(dataSize-(roundoffSize<<5))], devOutBuffer,
                                       device_offset=halfSize)
            result = dataOut[:(dataSize-(roundoffSize<<5))]

            self.cmdQueues[0].finish()
            self.cmdQueues[1].finish()

            
            encTime = time() - encStartTime


            if self.profiling:
                # Estimated execution time
		totTime = ((evKey.profile.end - evKey.profile.queued) + \
                    (evT0.profile.end - evT0.profile.queued) + \
                    (evT1.profile.end - evT1.profile.queued) + \
                    (evT2.profile.end - evT2.profile.queued) + \
                    (evT3.profile.end - evT3.profile.queued) + \
                    (evHalf2R.profile.end - evHalf1W.profile.queued))*1.0e-9
                
                print "File size: " + str(len(data)) + "Bytes"
                print "Execution time: " + str(totTime) + " Seconds"
                print "Execution time (+Python overhead): " + str(encTime) + " Seconds"
                print "Approximate throughput: ~" + str(float(len(data))/totTime) + " Byte/s"
        


        else:
            # Single chunk
            dataSize = len(data)
            if (dataSize>>4)%WG_SIZE:
                roundoffSize = WG_SIZE - ((dataSize>>4)%WG_SIZE)
                dataSize += roundoffSize<<4
            
            # Init host memory buffer and device memory buffer used for enable pinned-memory
            pinInBuffer = cl.Buffer(self.context, clmem.READ_ONLY|clmem.ALLOC_HOST_PTR, dataSize)
            pinOutBuffer = cl.Buffer(self.context, clmem.WRITE_ONLY|clmem.ALLOC_HOST_PTR, dataSize)
            devInBuffer = cl.Buffer(self.context, clmem.READ_ONLY, dataSize)
            devOutBuffer = cl.Buffer(self.context, clmem.WRITE_ONLY, dataSize)

            # Get numpy arrays used for filling and retrieving data from pinned-memory
            (dataIn,ev) = cl.enqueue_map_buffer(self.cmdQueues[0], pinInBuffer, clmap.WRITE,
                                                0, (dataSize,), np.uint8, 'C')
            (dataOut,ev) = cl.enqueue_map_buffer(self.cmdQueues[0], pinOutBuffer, clmap.READ,
                                                 0, (dataSize,), np.uint8, 'C')
            
            # Fill the array obtained from memory maps
            dataIn[:len(data)] = np.frombuffer(data, dtype=np.uint8)
            

            encStartTime = time()
            
            # Copy T-tables and key into global memory
            evKey = cl.enqueue_copy(self.cmdQueues[0], keyBuffer, key)
            evT0 = cl.enqueue_copy(self.cmdQueues[0], T0buff, T0)
            evT1 = cl.enqueue_copy(self.cmdQueues[0], T1buff, T1)
            evT2 = cl.enqueue_copy(self.cmdQueues[0], T2buff, T2)
            evT3 = cl.enqueue_copy(self.cmdQueues[0], T3buff, T3)


            #TODO: delete
            #expkeyBuff = cl.Buffer(self.context, clmem.WRITE_ONLY, 176)
            
            evDataW = cl.enqueue_copy(self.cmdQueues[0], devInBuffer, dataIn[:len(data)])
            
            # Launch kernel
            evK1 = program.aes_ecb(self.cmdQueues[0], (dataSize>>4,), (WG_SIZE,), keyBuffer,
                                   devInBuffer, devOutBuffer,
                                   T0buff, T1buff, T2buff, T3buff,
                                   #T0buff, T1buff, T2buff, T3buff, expkeyBuff,
                                   np.uint32(0))

            #TODO: delete
            #expKey = np.empty(176, dtype=np.uint8)
            #cl.enqueue_copy(self.cmdQueues[0], expKey, expkeyBuff)
            #print [hex(x) for x in expKey]
            
                                   
            evDataR = cl.enqueue_copy(self.cmdQueues[0], dataOut[:len(data)], devOutBuffer)

            encTime = time() - encStartTime

            result = dataOut[:len(data)]

            if self.profiling:
                # Estimated execution time
		totTime = ((evKey.profile.end - evKey.profile.queued) + \
                    (evT0.profile.end - evT0.profile.queued) + \
                    (evT1.profile.end - evT1.profile.queued) + \
                    (evT2.profile.end - evT2.profile.queued) + \
                    (evT3.profile.end - evT3.profile.queued) + \
                    (evDataR.profile.end - evDataW.profile.queued))*1.0e-9
                
                print "File size: " + str(len(data)) + "Bytes"
                print "Execution time: " + str(totTime) + " Seconds"
                print "Execution time (+Python overhead): " + str(encTime) + " Seconds"
                print "Approximate throughput: ~" + str(float(len(data))/totTime) + " Byte/s"
           

        return result


    # Defined only for benchmarking purposes
    # - AES using only global memory
    # - AES using constant memory for key and T tables
    def _encrypt_global(self, key, data):
        ''
        # Choose optimal parameters
        # TODO: automate choice of:
        # - bulk size;
        # - local work items size;
        # - global work items size

        # Build the kernel
        # TODO: perform only once
        kernelFile = open('aes_ecb_global.cl', 'r')
        kernelSrc = kernelFile.read()
        
        program = cl.Program(self.context, kernelSrc)
        program.build(devices=[self.gpuDevice])


        # Create buffers for key and T tables
        keyBuffer = cl.Buffer(self.context, clmem.READ_ONLY, size=16)


        # Single chunk
        dataSize = len(data)
        if (dataSize>>4)%WG_SIZE:
            roundoffSize = WG_SIZE - ((dataSize>>4)%WG_SIZE)
            dataSize += roundoffSize<<4
            
        inBuffer = cl.Buffer(self.context, clmem.READ_ONLY, dataSize)
        outBuffer = cl.Buffer(self.context, clmem.WRITE_ONLY, dataSize)
        expKeyBuffer = cl.Buffer(self.context, clmem.WRITE_ONLY, 44)

            
        # Fill the array obtained from memory maps
        dataIn = np.empty(dataSize, dtype=np.uint8)    
        dataOut = np.empty(dataSize, dtype=np.uint8)
        dataIn[:len(data)] = np.frombuffer(data, dtype=np.uint8)
        

        encStartTime = time()
            
        # Copy T-tables and key into global memory
        evKey = cl.enqueue_copy(self.cmdQueues[0], keyBuffer, key)
   
        evDataW = cl.enqueue_copy(self.cmdQueues[0], inBuffer, dataIn[:len(data)])
            
        # Launch kernel
        evK1 = program.aes_ecb(self.cmdQueues[0],
                               (dataSize>>4,), (WG_SIZE,),
                               keyBuffer, expKeyBuffer, inBuffer, outBuffer,
                               np.uint32(0))
                                   
        evDataR = cl.enqueue_copy(self.cmdQueues[0], dataOut[:len(data)], outBuffer)

        encTime = time() - encStartTime

        result = dataOut[:len(data)]

        if self.profiling:
            # Estimated execution time
            totTime = ((evKey.profile.end - evKey.profile.queued) + \
                       (evDataR.profile.end - evDataW.profile.queued))*1.0e-9
                
            print "File size: " + str(len(data)) + "Bytes"
            print "Execution time: " + str(totTime) + " Seconds"
            print "Execution time (+Python overhead): " + str(encTime) + " Seconds"
            print "Approximate throughput: ~" + str(float(len(data))/totTime) + " Byte/s"
           

        return result
